"Mappings between color spaces are ubiquitous in image processing problems such as gamut mapping, decolorization, and image optimization for color-blind people. Simple color transformations often result in information loss and ambiguities (for example, when mapping from RGB to grayscale), and one wishes to find an image-specific transformation that would preserve as much as possible the structure of the original image in the target color space. In this paper, we propose Laplacian colormaps, a generic framework for structure-preserving color transformations between images. We use the image Laplacian to capture the structural information, and show that if the color transformation between two images preserves the structure, the respective Laplacians have similar eigenvectors, or in other words, are approximately jointly diagonalizable. Employing the relation between joint diagonalizability and commutativity of matrices, we use Laplacians commutativity as a criterion of color mapping quality and minimize it w.r.t. the parameters of a color transformation to achieve optimal structure preservation. We show numerous applications of our approach, including color-to-gray conversion, gamut mapping, multispectral image fusion, and image optimization for color deficient viewers."

this paper is something I am particularly happy to share, as it is the first report related to my new research theme (wow, this reminds me that I should update my research page!). The coolest aspect of this topic is that, despite looking different from my previous work, it actually has a lot of points in common with it.

As some of you may know, many previous works of mine heavily relied on different implementations of the concept of similarity (e.g. similarity between tags, between tourism destinations, and so on). This concept has many interpretations, depending on how it is translated into an actual distance for automatic calculation (this is what typically happens in practice, no matter how "semantic" your interpretation is supposed to be).

One of the main problems is: in a rich and social ecosystem like the Web is, it is frequent to find different ways to define/measure similarity between entities. For instance, two images could be considered similar according to some visual descriptors (e.g. SIFT, or color histograms), to tags associated with them (e.g. "lake", "holiday", "bw"), to some descriptive text (e.g. a Wikipedia page describing what is depicted), metadata (e.g. author, camera lens, etc.), and so on. Moreover, people might not agree on what is similar to what, as everyone has their own subjective way of categorizing stuff. The result is that often there is no single way to relate similar entities. This is sometimes a limit (how can we say that our method is the correct one?) but also an advantage: for instance, when entities need to be disambiguated it is useful to have different ways of describing/classifying them. This is, I believe, an important step towards (more or less) automatically understanding the semantics of data.

The concept I like most behind this work is that there are indeed ways to exploit these different measures of similarity and (pardon me if I banalize it too much) find some kind of average measure that takes all of them into account. This allows, for instance, to tell apart different acceptations of the same word as it can be applied in dissimilar contexts, or photos that share the same graphical features but are assigned different tags. Some (synthetic and real-data) examples are provided, and finally some friends of mine will understand why I have spent weeks talking about swimming tigers ;-). The paper abstract follows:

"We construct an extension of diffusion geometry to multiple modalities through joint approximate diagonalization of Laplacian matrices. This naturally extends classical data analysis tools based on spectral geometry, such as diffusion maps and spectral clustering. We provide several synthetic and real examples of manifold learning, retrieval, and clustering demonstrating that the joint diffusion geometry frequently better captures the inherent structure of multi-modal data. We also show that many previous attempts to construct multimodal spectral clustering can be seen as particular cases of joint approximate diagonalization of the Laplacians."

… and the full text is available on ArXiv. Enjoy, and remember that --especially in this case, as this is mostly new stuff for me-- comments are more than welcome :-)

"Tag-based systems are widely available thanks to their intrinsic advantages, such as self-organization, currency, and ease of use. Although they represent a precious source of semantic metadata, their utility is still limited. The inherent lexical ambiguities of tags strongly affect the extraction of structured knowledge and the quality of tag-based recommendation systems. In this paper, we propose a methodology for the analysis of tag-based systems, addressing tag synonymy and homonymy at the same time in a holistic approach: in more detail, we exploit a tripartite graph to reduce the problem of synonyms and homonyms; we apply a customized version of Tag Context Similarity to detect them, overcoming the limitations of current similarity metrics; finally, we propose the application of an overlapping clustering algorithm to detect contexts and homonymies, then evaluate its performances, and introduce a methodology for the interpretation of its results."

The editor (John Wiley & Sons, Ltd.) requested not to directly make the paper available online. However I have "the personal right to send or transmit individual copies of this PDF to colleagues upon their specific request provided no fee is charged, and further-provided that there is no systematic distribution of the Contribution, e.g. posting on a listserv, website or automated delivery." So, just drop me an email if you want to read it and I will send it to you (in a non-systematic way ;-))

"Pictures about tourism destinations are part of the contents shared online through social media by travelers. User-generated pictures shared in social networks carry additional information such as geotags and user descriptions of places that can be used to identify groups of similar destinations. This article investigates the possibility of defining destination similarities relying on implicit information already shared on the Web. Additionally, the possibility of recommending one city on the basis of a given set of pictures is explored. Flickr. com was used as a case study as it represents the most popular picture sharing website. The results indicate that it is possible to group similar destinations according to picture-related information, and recommending destinations without requiring users' profiles or sets of explicit preferences.".

"The amount of geo-referenced information on the Web is increasing thanks to the large availability of location-aware mobile devices and map interfaces. In particular, in photo
collections like Flickr the coexistence of geographic metadata and text-based annotations (tags) can be exploited to infer new, useful information. This paper introduces a novel method to generate place profiles as vectors of user-provided tags from Flickr geo-referenced photos. These profiles can then be used to measure place similarity in terms of the distance between their matching vectors. A Web-based prototype has been implemented and used to analyze two distinct Flickr datasets, related to a chosen set of top tourism destinations. The system has been evaluated by real users with an online survey. Results show that our method is suitable to define similar destinations. Moreover, according to users, enriching place description with information from user activities provided better similarities".

"Ontologies are the basic block of modern knowledge-based systems; however the effort and expertise required to develop them are often preventing their widespread adoption. In this chapter we present a tool for the automatic discovery of basic ontologies –we call them seed ontologies– starting from a corpus of documents related to a specific domain of knowledge. These seed ontologies are not meant for direct use, but they can be used to bootstrap the knowledge acquisition process by providing a selection of relevant terms and fundamental relationships. The tool is modular and it allows the integration of different methods/strategies in the indexing of the corpus, selection of relevant terms, discovery of hierarchies and other relationships among terms. Like any induction process, also ontology learning from text is prone to errors, so we do not expect from our tool a 100% correct ontology; according to our evaluation the result is more close to 80%, but this should be enough for a domain expert to complete the work with limited effort and in a short time".

Lately I am experimenting with spectral clustering. I find it a very interesting approach (well, family of) to clustering and I think that the paper that most helped me to have a grasp of it was "A Tutorial on Spectral Clustering", by Ulrike von Luxburg. In case you are curious about it, here is its abstract:

"In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed".

While reading von Luxburg's paper, I took some notes that might be handy if you want to have a brief summary of the main concepts or explain them to somebody else. I actually reused them for a class in PoliMI, empirically demonstrating that in research nothing is unuseful ;-) Here are the slides I made, enjoy!

"Pictures about tourism destinations are part of the contents shared online through social media
by travelers. Additional picture information, such as geo-tags and user description of a place,
can be used to create groups of similar destinations. This paper investigates the possibility of
defining destination similarities based on implicit information already shared on the Web.
Flickr.com was used as a case study as it represents the most popular picture sharing website.
Results show the possibility to group similar destinations based on visual components,
represented by the contents of the pictures, and the related tag descriptions".

I have recently attended the "Promoting your academic profile on the Web" at USI. It was a good workshop (thanks Lorenzo and Nadzeya!) and allowed me to get an idea about how well I am communicating who I am and what I do to the world. As you might know already, I think this is pretty important if your aim is to make your research (or in general your work) open and accessible to everyone.

The introspection that followed the workshop made me realize how often I put doing stuff before communicating it. I mean, of course I do that when I publish a paper, but too often I do not let others know that the paper I wrote exists! For instance, I realized that as of today I had not updated my publication list with any of the work published in 2012... D'oh :-)

For this reason I have decided not only to update that list, but also to write one post for each new paper, making the abstract and additional material available. In this post, instead, I will keep an updated list of links to this year's publications, so you can just watch this page and see when something new has been posted.